A Novel Transformer Fault Diagnosis Method Based on Gross Error Examination

2011 ◽  
Vol 291-294 ◽  
pp. 2779-2786
Author(s):  
Jie Su ◽  
Xu Guang Wang
Keyword(s):  
Fault Diagnosis ◽  
Ratio Method ◽  
Fault Probability ◽  
Gross Error ◽  
Measurement Series ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Transformer Fault ◽  
Gas Ratio ◽  
Fault Characteristic

The study applied the gross error theory to the transformer fault diagnosis, proposing a method for transformer fault diagnosis by integrating the gross error examination and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measurement series of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

Transformer Fault Diagnosis Method Based on Gross Error Examination

2012 ◽  
Vol 482-484 ◽  
pp. 2350-2354
Author(s):  
Jie Su ◽  
Xu Guang Wang
Keyword(s):  
Fault Diagnosis ◽  
Ratio Method ◽  
Fault Probability ◽  
Gross Error ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Transformer Fault ◽  
Gas Ratio ◽  
Fault Characteristic

This paper proposes a gross error judgment criterion and diagnoses the transformer fault by integrating the gross error judgment criterion and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measured values of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

scholarly journals An Intelligent Fault Diagnosis Method for Transformer Based on IPSO-gcForest

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kezhen Liu ◽  
Shizhe Wu ◽  
Zhao Luo ◽  
Zeweiyi Gongze ◽  
Xianlong Ma ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Characteristic Parameter ◽  
Economic Losses ◽  
Stable Operation ◽  
Diagnosis Method ◽  
Transformer Fault ◽  
Ipso Algorithm ◽  
Better Than ◽  
Fault Characteristic

Transformers are the main equipment for power system operation. Undiagnosed faults in the internal components of the transformer will increase the downtime during operation and cause significant economic losses. Efficient and accurate transformer fault diagnosis is an important part of power grid research, which plays a key role in the safe and stable operation of the power system. Existing traditional transformer fault diagnosis methods have the problems of low accuracy, difficulty in effectively processing fault characteristic information, and superparameters that adversely affect transformer fault diagnosis. In this paper, we propose a transformer fault diagnosis method based on improved particle swarm optimization (IPSO) and multigrained cascade forest (gcForest). Considering the correlation between the characteristic gas dissolved in oil and the type of fault, firstly, the noncode ratios of the characteristic gas dissolved in the oil are determined as the characteristic parameter of the model. Then, the IPSO algorithm is used to iteratively optimize the parameters of the gcForest model and obtain the optimal parameters with the highest diagnostic accuracy. Finally, the diagnosis effect of IPSO-gcForest model under different characteristic parameters and size samples is analyzed by identification experiments and compared with that of various methods. The results show that the diagnostic effect of the model with noncode ratios as the characteristic parameter is better than DGA data, IEC ratios, and Rogers ratios. And the IPSO-gcForest model can effectively improve the accuracy of transformer fault diagnosis, thus verifying the feasibility and effectiveness of the method.

A Calculation Method of Transformer Fault BPA Based on Spatial Interpolation and IEC60599

2014 ◽  
Vol 631-632 ◽  
pp. 563-567
Author(s):  
Wei Hua Zhang ◽  
Jin Sha Yuan ◽  
Tie Feng Zhang ◽  
Hong Yang
Keyword(s):  
Calculation Method ◽  
Spatial Interpolation ◽  
Evidence Theory ◽  
Ratio Method ◽  
Characteristic Space ◽  
Calculation Results ◽  
Transformer Fault ◽  
Engineering Projects ◽  
Gas Ratio ◽  
Fault Characteristic

The diagnostic conclusions by IEC60599 gas ratio method cannot be directly applied to DS Evidence Theory for information fusion. In order to solve the problem, this research proposed a calculation method for transformer fault BPA based on spatial interpolation. Firstly, the fault characteristic space defined by IEC60599 gas ratio method was analyzed. Based on the analysis results, the entire fault characteristic space was divided into several rectangular units through spatial discretization. Next, the BPA function of faults in each unit was formulated by 8-node hexahedral unit interpolation. In this way, the continuous BPA function of faults in the entire space was obtained. This method not only met the requirements of BPA function but also enabled the numerical reflection of various fault possibilities in the calculation results. Thus, this method extended the application of IEC60599 gas ratio method. With example calculation, the correctness and feasibility of this method in actual engineering projects were verified.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Research on Power Transformer Fault Forecast

2011 ◽  
Vol 204-210 ◽  
pp. 1553-1558
Author(s):  
Rui Rui Zheng ◽  
Ji Yin Zhao ◽  
Min Li ◽  
Bao Chun Wu
Keyword(s):  
Time Series ◽  
Fault Location ◽  
Power Transformer ◽  
Forecast Model ◽  
Ratio Method ◽  
Gray Model ◽  
Artificial Immune ◽  
Diagnosis Method ◽  
Type Data ◽  
Transformer Fault

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

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